Education

• MS in Computational and Mathematical Engineering, Stanford University, 2024 - Current
  • Mathematical and Computational Finance Track
    
    
• BMATH, University of Waterloo, 2019 - 2024
  • Major: Pure Mathematics, Statistics and Mathematical Finance
  • Minor: Computer Science (dropped out from major due to high tuitions)
    
    

Work experience

• Oct 2025 - Now: Member of Technical Staff at Liquid AI
  • Location: San Francisco, CA, United States
  • Team: Post-Training
  • Model Contribution (as of January 2026): LFM2.5-1.2B-instruct
    
    
• Summer 2025: Member of Technical Staff ‑ Machine Learning Research Scientist Intern at Liquid AI
  • Location: San Francisco, CA, United States
  • Team: Post-Training
  • Outcome: LFM2-1.2B-Tool
    
    
• Fall 2023: Quantitative Analyst Intern at Scotiabank
  • Location: Toronto, ON, Canada
  • Team: Interest Rates
    
    
• Fall 2022: Quantitative Researcher Intern at UBS Securities
  • Location: New York, NY, United States
  • Team: Credit Algorithmic Trading
    
    
• Winter 2022: Data Scientist Intern at Liberty Mutual
  • Location: Toronto, ON, Canada
    
    

Research experience

• April 2025 - June 2025: Large Language Model Retrieval‑Augmented Generation at Stanford University
  • Location: Stanford, CA, United States
  • Designing and implementing a retrieval‑augmented generation (RAG) pipeline. Improved LLMs performance with retrieval‑based feed‑ back, optimizing response relevance through reward modeling.
    
    
• May 2023 - April 2024: Bayesian Inference in Bioinformatics at University of Waterloo
  • Location: Waterloo, ON, Canada
  • Collaborated researchers and wrote the Bayesian Non‑parametric Methods parts of the research paper in bioinformatics fields. Prepared research reports and implemented algorithms, designed data collection protocols.
  • Made use Python to implement state‑of‑the‑art inference techniques, including Markov Chain Monte Carlo (MCMC), Dirichlet Process Mix‑ ture Models (DPMM) to estimate model parameters and latent variables.
    
    
• September 2023 - Decemember 2023: Self‑Similar Stochastic Process University of Waterloo
  • Location: Waterloo, ON, Canada
  • Engaged in theoretical investigations to prove mathematical theorems in research papers, and explore new approaches to solve challeng‑ ing problems in probability theory
  • Construct and implement the probabilistic models based on the ideas from research papers, conduct simulations and analysis